Automated identification of compressive stress and damage in concrete specimen using convolutional neural network learned electromechanical admittance. (15th May 2022)

• Record Type: Journal Article
• Authors: Ai, Demi; Mo, Fang; Han, Yihang; Wen, Junjie
• Is Part Of: Engineering structures. Volume 259(2022)
• Publication Date: 2022-05-15
• Subjects: Electromechanical admittance (EMA) -- PZT transducer -- Two-dimensional convolutional neural network -- Compressive stress -- Damage identification -- Concrete structure; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114176
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: 2-D CNN approach for detection of compressive stress/damage in concrete was proposed. Proof-of-concept experiment was conducted on a concrete specimen under compressive test. Qualitative detection of stress and damage was analysed via characteristics of EMA spectra. Quantifiable assessment of stress level and damage severity was attempted using the model. The approach was of high accuracy and efficiency for compressive stress/damage detection. Abstract: For the first time, this paper proposed a simple two-dimensional convolutional neural network (2-D CNN) integrated with electromechanical admittance (EMA, inverse of impedance) to detect compressive stress and load-induced damages of concrete cubic structure subjected to applied loading, which was attempted to solve the difficulty for stress/damage quantification merely based on traditional EMA technique. It automatically resolved the raw EMA signatures with no need of complex transformation or manual feature extraction. In the experimentation, the whole process of the structural states from initial loading to fatal failure was monitored via using surface-bonded piezoelectric ceramic lead zirconate titanate (PZT) transducer. Independent EMA databases were established at each stress/damage state of the structure. Qualitative detection of stress development and cracking damage progression as three stages of failure mode was analysed via using characteristics of EMA spectra encompassing resonant frequency and amplitude values. Quantifiable assessment of stress level and damage severity was attempted using the raw EMA signatures learned by the proposed CNN model without signal pre-processing. Experimental results demonstrated that EMA signature was sensitive to stress variations in concrete and its cracking, expansion and propagation. The proposed approach possessed excellent accuracy and efficiency superior to traditional root mean square deviation (RMSD)-based one for quantification on compressive stress and damage state changes in the specimen, which potentially provided an alternative paradigm of data-driven stress/damage identification in concrete structures.